2022
2023

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NOE ( Network Of Excellence ) Project
Project for Fostering and Promoting
Statistical Thinking

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■ History——Message from Director-General

Since our founding on June 5, 1944, the Institute of Statistical Mathematics(ISM) has been dedicated to statistical theories and their applications to elucidate and design behaviors and other phenomena of interest. We express
our sincerest gratitude to the many industry, government, and academia
stakeholders who have supported our statistical and mathematical science
research for almost 80 years. The ISM faculty and staff remain committed toour mission, engaging in new research projects, and providing academic career development opportunities.
Today, the world seeks to identify an ideal form of a data-driven society,and Japan is in great need of data scientists with excellent problem-solving
and goal-achieving capabilities. In fiscal year (FY) 2021, ISM received funding
for five years from the Ministry of Education, Culture, Sports, Science, andTechnology (MEXT) to implement “The Project for Training Experts in Statistical Sciences.” Under this project, we launched the Center for Training Professors in Statistics in January 2022 as a teaching platform similar to ISM’s
School of Statistical Thinking, which has been home to various levels of statistical scientists. The new center offers two-year faculty development programs to empower junior andmid-career members of the 21 academic institutions participating in the project. These
member institutions have formed a consortium to jointly develop and implement a master’s-level statistical education program. The junior and mid-career faculty members whocomplete the two-year program will lead their institutions’ career development ecosystem,
where graduate students will receive world-leading statistical education and training.
Two additional five-year projects proposed by ISM were also approved by MEXT in FY
2021. One is the “Creation of data infrastructure for data-driven polymer materials research”
project, which was approved under the Program for Promoting Research on the Supercomputer Fugaku. This project is represented by ISM’s Data Science Center for Creative Designand Manufacturing. The other is the “New developments in space-time earthquake forecasting and monitoring: from long-term to real-time” project, which is represented by ISM’s
Risk Analysis Research Center. This research plan was approved under the Seismology
TowArd Research innovation with data of Earthquake Project (STAR-E Project). We havespent a long time implementing the Network of Excellence (NOE) Project, an inter-institutional effort to advance the theoretical foundations of data science and to build and activate
the relevant research networks. These activities have opened the door to new concepts,
methodologies, and applied research opportunities. Such advancements demonstrate thepractical utility of statistical and mathematical sciences.
With regard to the novel coronavirus disease 2019 (COVID-19), we launched the COVID-19
Response Project in FY 2020, which addresses statistical and mathematical methods to reduce the risks of COVID-19 infection. Since the COVID-19 pandemic continues with no end
in sight, ISM’s activities involving overseas scientists and in-person interactions have been
severely disrupted. Under these circumstances, we make the best available choices utilizing the experience we gained in virtual research meetings and other contingency measures.
ISM’s Fourth Medium-Term Plan has been in effect since April 2022. This plan guides ISM’s
future research and educational orientations. This plan will not only ensure that the research outputs of ISM inform and benefit society but also that ISM researchers are wellequipped with the competencies to advance the frontiers of basic science and apply advanced knowledge to practical contexts. We cordially ask for your continued support andcooperation.

Hiroe Tsubaki

Director-General
The Institute of Statistical Mathematics1Institute Overview

Basic Research
Department of Statistical Modeling
The Department of Statistical Modeling works on structural modeling of physical phenomena related to numerous factors,
and conducts research on model-based statistical inference methodologies. By means of model-based prediction and control,modeling of complex systems, and data assimilation, the department aims to contribute to the development of modeling intel­
ligence in many fields.

■ Prediction and Control Group ————————————————————————————————————————————The Prediction and Control Group works on the development and evaluation of statistical models, which function effectively
in terms of prediction and control of phenomena, decision making, and scientific discoveries. These efforts involve data
analysis and modeling related to phenomena that vary across time and space.

■ Complex System Modeling Group —————————————————————————————————————————The Complex System Modeling Group conducts studies aimed at discovering the structures of complex systems, such as
nonlinear systems and hierarchical networks, through statistical modeling. For these purposes, the group also considers
Monte Carlo simulations, discrete mathematics, and computer science.

■ Data Assimilation Group ———————————————————————————————————————————————The Data Assimilation Group works on the development of data assimilation techniques, which are procedures aimed
at combining information derived from large amounts of observations and a numerical simulation model. By developing
computational algorithms and high-performance parallel computing systems, the group aims to build a next-generation
simulation model that can predict the future in real time.

Department of Statistical Data Science
The Department of Statistical Data Science conducts research on data design methods aimed at managing uncertainty
and incompleteness of information, quantitative methods for evidence-based practice, and related data analysis methods.
Moreover, the department investigates methods for inferring the latent structures in target phenomena from observation data.

■ Survey Science Group ————————————————————————————————————————————————The Survey Science Group promotes research on the design of statistical surveys, development of statistical analysis methods on survey data, privacy protection in official statistics, and their applications. By exploring complex phenomena in various
fields, the group also aims to contribute to practical applications in academia and policy-making through social surveys.

■ Metric Science Group —————————————————————————————————————————————————The Metric Science Group conducts research aimed at identifying and evaluating statistical evidence through quan­tification of phenomena that have not been measured thus far, as well as efficient information extraction from large
databases. The group investigates related methods and develops methods for analyzing the collected data. By working
on applied research in various fields of real science, the group aims to advance practical, applied, statistical mathematicalresearch based on evidence.

■ Structure Exploration Group —————————————————————————————————————————————The Structure Exploration Group conducts research on statistical science aimed at inferring the latent “structure” behindvarious target phenomena in biology, physics, and social science, based on observational data. The group focuseson machine learning, Bayesian reasoning, experimental design methods, and spatial-temporal analysis methods toinvestigate micro/meso/macroscopic and spatial-temporal dynamic structures in target phenomena.

Department of Statistical Inference and Mathematics
The Department of Statistical Inference and Mathematics carries out research into general statistical theory, statisticallearning theory, optimization, and algorithms for statistical inference.

■ Mathematical Statistics Group ———————————————————————————————————————————The Mathematical Statistics Group is concerned with aspects of statistical inference theory, modeling of uncertain
phenomena, stochastic processes and their application to inference, probability and distribution theory, and the related
mathematics.

■ Learning and Inference Group ————————————————————————————————————————————The Learning and Inference Group develops statistical methodologies to describe the stochastic structure of data math­ematically and clarify the potential and the limitations of the data theoretically.

■ Mathematical Optimization Group —————————————————————————————————————————The Mathematical Optimization Group focuses on mathematical theory and practical applications of optimization andcomputational algorithms together with underlying numerical or functional analysis and discrete mathematics.

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NOE-type Research
Risk Analysis Research Center
Risk Analysis Research Center is pursuing a scientific approach to managing uncertainties and risks in society, which have increased
with the growing globalization of society and economy. Our research projects are mainly seismology, finance, resources, environmentology,
database development, risk mathematics, and spatiotemporal modeling. The Center also manages a network organization for risk
analysis to facilitate research collaboration across different organizations with the common goal of creating a safe and resilient society.

Research Center for Statistical Machine Learning
Machine learning is a research field associated with autonomous systems that can learn their behavior from data. This field is basedon both the statistical science concerning inference from data and computer science concerning efficient algorithms. It can be applied
to broad disciplines ranging engineering and information science to natural science, such as robotics and brain sciences. Our research
center aims at supporting the academic community of this field, as well as producing influential research through various joint projects.

Data Science Center for Creative Design and Manufacturing
We aim to foster new scientific methods for innovative design and manufacturing. Various fields in manufacturing are now facing a
revolutionary period. Population reduction and globalization are bringing dramatic changes in the industrial structure in Japan.
Countries around the world has actively developed their growth strategies utilizing data science as a driving force. We have accumulated
state-of-the-art technologies in data science here. We are devoted to foster and practice advanced methods in data science for design
and manufacturing.

Research Center for Medical and Health Data Science
Research Center for Medical and Health Data Science aims to facilitate statistical data science research that covers medical studies,drug developments, health care, and public health. Our research projects involve fundamental mathematics and compu­tational science
for medical applications, applied methodology for basic, clinical and social medicine, and modern technology such as artificial
intelligence, machine learning, and big data analyses. Furthermore, our research center aims at constructing a research network of theacademic community of this field, as well as offering advanced statistical education programs.

Professional Development
School of Statistical Thinking
The mission of the School of Statistical Thinking is to plan and implement various programs for statistical thinking, from extension
courses to a professional development program. The researchers affiliated with the school are often involved with specific data analysis
projects, which help them to gain hands-on knowledge of data science. We expect such an experienced researcher will play an active role
as a number-cruncher, as a modeler, or as a project coordinator.

Center for Training Professors in Statistics
The Center has established the nationwide consortium of universities and promotes the Project for Training Experts in Statistical
Sciences to address the critical shortage of professors in statistics, which form the core of data science. Over the next 5 years, theCenter will develop at least 30 university professors in statistics, who will train statistics experts at member universities of theconsortium. The Center aims to establish a positive cycle of professional development.

Research Support
Center for Engineering and Technical Support
The Center for Engineering and Technical Support assists academics and their collaborators in many ways: managing
computer systems and networks, editing and publishing journals, maintaining the library, and managing tutorial programs.

■ Computing Facilities Unit The Computing Facilities Unit is in charge of managing computer facilities and scientific software.
■ Computer Networking Unit The Computer Networking Unit is responsible for computer networking and its infra­structure, andnetwork security.
■ Information Resources Unit The Information Resources Unit is responsible for maintaining a library and an electronic repository,
and is in charge of planning statistical tutorial programs open to the public.
■ Media Development Unit The Media Development Unit is in charge of publishing and editing of research results and PR brochures.

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Research Topics
Risk Analysis Research Center

Data Science in Seismology and Geodesy■ Data science in seismology and geodesy
In seismology and geodesy, continuous data obtained by a wide variety of instruments are accumulated. Since the temporal and spatial granularity of seismic and geodetic data is becoming finer and finer withthe development of measuring techniques, the volumeof data is a serious issue. In recent years, many research
projects have been launched worldwide to analyze
such massive data and to obtain seismic and geodetic
knowledge by utilizing new data-science technologies
in addition to conventional ones. Here, I would like tointroduce two of the projects in which I participate.

■ Earthquake detection by deep learning
Detecting earthquakes in the vast amount of continuous waveform records is the first step in seismology.

Deep learning is one of the de facto standard data analysis methods, and earthquake detection using deeplearning has been successful and greatly outperformed
existing detection methods. However, most of the researches have utilized only waveform data at a single
observation station. In addition to seismic signals, local
environmental noises near the ground are containedin the observed waveforms, and thus there is a potential limitation in preventing false positives by using
only a single observation point. We proposed a method
for automated earthquake detection by using convolutional neural networks for continuous data at multiple
stations. We applied the proposed method to the Metropolitan Seismic Observation network (MeSO-net),and succeeded in suppressing the false detection rateand capturing very weak earthquake signals overlooked by humans (Figure 1).
Figure 1: Automated earthquake
detection by convolutional neural network using
continuous waveform records at multiple observation stations ( Yano etal., JGR Solid Earth, 2021).

■ Slow slip detection by sparse modeling

Figure 2: Inversion results of potential slow slip events detected by the proposed method ( Yano and Kano, ESSOAr ).

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The phenomenon of “slow slip event,” in whicha fault ruptures over a long period (from days toyears) and releases strain energy without emitting seismic waves, has been attracting attention in seismology and geodesy. This phenomenon has been found in tectonic zones worldwide,and provides a clue to understanding the surrounding stress environment. Slow slip event
often does not lead to clear crustal deformationthat can be visually picked. So, developing an
automatic detection method with high detection accuracy is necessary.
We have developed an automatic detection
method of slow slip events from GPS data using“sparse modeling” and “combined p-value method.” By applying the developed method to data
in western Shikoku, we found new 12 events inabout two years (Figure 2).
Keisuke Yano

Risk Evaluation for a Specific Predator Species for a Specific Prey Population■ Prey-predator system
For a prey-predator system which is more easily investigated, the predator or the prey? More concretely,
which is more easily estimable, the proportion of a specific prey species in food resource of a predator species,or the proportion of a specific predator species in mortality of a prey population? The answer is the latter. The
former is available, for example, investigating stomach
contents, chasing individuals, and so on, whereas forthe latter, we should chase hundreds of preys from thebirth to death, which is nearly impossible.

■ Risk evaluation for predator control
Occasionally, predators were excluded to protect endangered preys, or introduced to reduce harmful preys.
In order to evaluate the risk for these, we need to know
the contribution of that predator species in the mortalityof preys.

■ Autotomy: evidence of predation
As is well-known, a lizard cut the tail to escape from a
predator and the tail is regenerating (autotomy). An individual with a regenerating tail should provide information on the predator’s contribution in the prey mortality.
Conducting experiments in a laboratory, we may
know the proportions of three fates of a prey when attacked by a predator, consumed, escape by autotomy
and escape without autotomy. Conducting a capturemark-recapture survey, we may find an individual withan intact tail when released and with a regenerating tailwhen recaptured. The number of such individuals mul-

tiplied by the ratio between consumed and escape with
autotomy in the experiment should give us an estimateof mortality by a specific predator. Because recapture
data provides us an estimate of the mortality (Fig. 1),
their differences corresponds to the contribution of thespecific predator species to the mortality of the preys.

■ Snail-snake system in Okinawa
Such a study was carried out for a specific snail species and a specific snake species in which the snail may
escape from the snake’s attack by autotomy. Integrating
the experiment, a capture-mark-recapture survey and ahierarchical Bayesian model, the encounter rate to thesnake species per month was estimated as 0.033, andthe proportion of the snake species in the mortality until
maturation (it is about 1.5 year) as about 40% (Fig. 2).
red: deadyellow: autotomy
green: intact

Figure 2: (a) An example of the proportions of the three fateswhen a snail was attacked by a snake. The real
proportions are more complicated because thefates depend on attributes of snails.
(b) A part of the recapture records.
(c) Let u be the encounter rate to a predator, a the probability of escape by autotomy, and p the recapture
probability. If an individual with a regenerating tailis recaptured, its probability is not a simple productof uap, but the probability that the prey survived( =did not meet any mortality event ) must be multiplied.

■ Statistical model, field works and experiments
for risk evaluation
Figure 1: Let s be the survival probability and p the recapture
probability. If recapture censuses were done twice,there are four patterns ( in which  indicates successin recapture and  failure ). The probability that we obtain each pattern can be derived as below (likelihood).
From these equations, we may estimate the survival
and the recapture probabilities.

There are variety of prey-predator systems on a globe.
If field works, experiments and statistical models areadequately integrated to capture the characteristics ofthe system, we will be able to obtain estimates about therisk of controlling the predators.
Kenichiro Shimatani
5Research Center for Statistical Machine Learning

Assessment of Significance of Gravitational Wave Signals■ What is the gravitational wave?
In 1915, Albert Einstein published the paper on the general
theory of relativity, which describes gravity as differential
geometry of spacetime. The theory is based on a nonlinear
partial differential equation for metric. Since small strain of
a spacetime follows a linear wave equation, Einstein predicted existence of the gravitational wave, which is producedby mass acceleration and propagates at the speed of light.
The existence was certain, because decay of an orbit of abinary star system can be explained by loss of energy by thegravitational wave radiation. Nevertheless, direct observation of the gravitational wave had been difficult, because
strain of space time is extremely small: it is typically smaller
than 10-20 when we observe gravitational waves producedby astronomical phenomena. Observation of the gravitational wave has several importance, including demonstration of cutting-age measurement technology, establishmentof astronomical observation device other than electromagnetic waves, and testing of theories of gravity.

■ Assessment of significance of gravitational
wave signals
In September 2015, the gravitational wave telescope LIGO
in the United States, consists of two detectors, firstly detected a gravitational wave signal. The gravitational wave wasproduced by merger of black hole binary and was named theGW150914. Since a gravitational wave produced by mergerof a binary star system is well described by the theory ofgeneral relativity, we fit wave forms computed by the theory
to the observed wave and use a test statistic constructed byusing the likelihood ratio (Figure). Significance of a gravitational wave signal is represented by the return period; theGW150914 is once in 0.2 million years if it was a noise. Here,the estimation under the noise assumption is done by shifting in time of the time series obtained by the two detectors.
The posterior probability is also used. If a candidate of agravitational wave signal fulfils criteria for the return period
and the posterior probability, the candidate is considered asignal. Dozens of candidates have already been found, andthe assessment of the significances is an issue. The gravitational wave telescope KAGRA is constructed in Japan, andthe observation is in preparation (Photo).

■ Estimation of the false discovery rate
Assessment of significance when we have multiple signal
candidates is a typical issue in data analysis. The false discovery rate, formally introduced by Benjamini and Hochberg in 1995 (expected proportion of noises in events calledsignal) is frequently used. The distribution of the detection
statistic is unknown, but we can estimate p-values. Using
the fact that p-vales of noises are uniformly distributed, we
proposed a method to estimate the false discovery rate if wecall a candidate a signal. Compared with estimation of theposterior probability, the proposed method is free from distributional assumption on the detection statistic and computationally cheap. Candidates with high significance are
significant under any criteria, but others are not. This is acollaboration with Drs. Hirotaka Yuzurihara and Hideyuki
Tagoshi from ICRR, The University of Tokyo, and supportedby the ISM Cooperative Research Program. The details are
published in the open access journal, Progress of Theoreticaland Experimental Physics, Volume 2021, Number 12.

Shuhei Mano

Figure: The wave form of the gravitational wave GW150914( Courtesy Caltech/MIT/LIGO Laboratory ).

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Photo: A duct for laser of the gravitational wave telescope KAGRA( Photo courtesy: KAGRA Observatory, ICRR, The University of Tokyo).

Research Topics

Efficient Method for Estimating Causal Structures■ Causal Discovery
Suppose that among many variables, there is a causal
structure such that a change in A causes changes in B and C,a change in B causes a change in D, and so on. The problem
is to identify this causal structure from the data. The wellknown correlation structure alone cannot identify such a
causal structure because the direction of influence is unknown. However, if we assume some structure behind thedata, we can identify the causal structure from the data.
Using this idea, there is a research topic called causal search,
in which causal structures are identified from data.

Figure 1: Causal Inference.

LASSO. Therefore, we have considered taking a sparse estimation approach to LiNGAM.

■ Penalized likelihood method
LiNGAM makes good use of the ideas of independent
component analysis. The parameter estimation is based on
maximization of the non-Gaussian likelihood used in independent component analysis. If we add a sparsity penalty to
the likelihood and consider a penalized likelihood maximization, the sparse estimation of LiNGAM seems to be possible. Such a study has already been done. However, in paststudies, the orthogonalization, which is important in independent component analysis, was not well incorporated in
the penalized likelihood maximization. In addition, if we simply incorporate not only orthogonalization but also sparse
penalty at the same time, it is difficult to achieve a sparse
structure sufficiently because they are not compatible. In
this study, we have solved this problem by using the alternating direction multiplier method and relaxation of the parameter structure, which enables efficient sparse structure
identification.

■ Numerical experiments■ Linear Non-Gaussian Acyclic Model ( LiNGAM )One of the most famous methods in causal discovery is amethod that enables causal structure identification by assuming acyclicity and noise non-normality under linear
structural equation models. This method is based on theidea of independent component analysis, and is named
Linear Non-Gaussian Acyclic Model(LiNGAM).

We compared our method with many other methods bynumerical experiments. In addition to the standard ICALiNGAM and DirectLiNGAM, we also compared our methodwith Zheng+ (2018, GOLEM) and Ng+ (2020, NOTEARS),
which were recently published in NeurIPS. The good performance of the proposed method was confirmed.

Figure 2:
Acyclic Linear Structural
Equation.

■ LiNGAM with sparse structure
We have studied an efficient method for estimating thecausal structure in LiNGAM under the assumption that the
regression parameters of the linear structural equation model are sparse. When the linear structural equation is acyclic,
half of the regression parameters of the linear structural
equation are zero. Therefore, it is natural to assume a sparse
structure. Also, in high-dimensional data, it is natural toassume that the regression parameters are sparse, as in

Figure 3:
Numerical
Experiments.

This work is summarized in the following paper.
References: Harada, K. and Fujisawa, H. ( 2021). Sparse Estimationof Linear Non-Gaussian Acyclic Model for Causal Discovery. Neurocomputing, Vol. 459, 223-233.

Hironori Fujisawa
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Data Science Center for Creative Design and Manufacturing

Creation of Polymer Properties Database Using Automated Molecular
Dynamics Simulation■ Exploration of polymeric materials with
innovative properties
Polymeric materials, such as plastics, are widelyused because of their lightness, ease of processing,and high viscoelasticity. On the other hand, the parameter space for material design is vast, so there is a needto establish an efficient material exploration method
for further functionalization. The aim of the project is
to discover new polymeric materials with innovative
properties. Our group is participating in a research
program of the Japan Science and Technology Agency(JST) for Nano-enabled Thermal Control, which started in 2019. In addition, in 2021, the Program for Promoting Research on the Supercomputer Fugaku “Creationof Data Infrastructure to Transform Data-Driven Polymer Materials Research” was accepted.

■ Machine learning technologies for automatedmolecular design
We applied a machine learning algorithm to PoLyInfo to design polymers with high thermal conductivity. For amorphous polymers measured at aroundroom temperatures, only 28 samples were recorded at
the database. To overcome the difficulty of the limited
data, we first trained machine learning models using
the dataset of other related thermophysical properties
where sufficient data were available, and then these
pretrained models were finetuned with the small dataset of thermal conductivity. This is a machine learningtechnique called transfer learning. By solving the inverse problem of the resulting model, we found andsynthesized new polymers that reached a thermal con-ductivity of 0.41 W/mK.

■ Lack of open data for data-driven research
The amount of data in materials research is muchless than in other applied fields of data science. There
are three main reasons for this: (1) the high cost ofdata production; (2) the difficulty of creating common
data due to the diversity of demand; and (3) the importance of the prevention of information leakage to competitors. These factors have hindered the developmentof open databases. In particular, for polymers, there isno database that can be used in data-driven research,other than the PoLyInfo database mentioned above.

■ Creation of polymer property database with
automated molecular dynamics simulation
We are developing RadonPy, an automated highthroughput calculation system using molecular dynamics (MD) simulations for polymer properties. Currently,
we are developing a polymer properties database using RadonPy. Our ultimate goal is to build a databasethat covers a broad array of polymeric properties formore than 100,000 polymers. Once this goal is achieved,
we will be able to observe the joint distribution of multiple properties in the vast material space. For example, we aim to comprehensively identify polymer structures distributed at around the Pareto boundaries
between multiple properties. Furthermore, in the process of data accumulation, unique materials that canovercome the Pareto boundary may be discovered. We
will accomplish this mission as a joint research project
under industry-academia partnerships.

Yoshihiro Hayashi

Figure: Automated calculation of polymer properties using molecular dynamics simulation.

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Research Topics

Search for New Quasicrystals Using Machine Learning■ Accelerating the discovery of new quasicrystals
based on data science
Quasicrystals are a class of materials that do nothave the translational symmetry of ordinary periodic
crystals, but have a high degree of order in their atomic arrangement. The first quasicrystal was discoveredby Schechtman in 1984. Over the next 35 years, morethan 100 types of quasicrystals were found, and quasicrystals were placed as the third class of solids alongwith ordinary crystals and amorphous. However, thepace of the discovery has slowed significantly in recent years. We are participating in the Grant-in-Aid for
Scientific Research on Innovative Areas, “Hypermaterials: Innovation of Materials Science in Hyperspace”),
which started in 2019 (Prof. Ryuji Tamura, Tokyo University of Science). We will accelerate the discovery of
new quasicrystals by introducing machine learningtechniques to the field.

■ Prediction of chemical compositions to form
quasicrystals
We tackled the problem of predicting quasicrystals
using a simple workflow of supervised learning. The
input variable of the model is the chemical composition. The output variables represent class labels indicating “quasicrystals”, “approximant crystals”, and “others” including ordinary periodic crystals. As training

data, we used the chemical compositions of quasicrystals, approximants, and ordinary crystals that havebeen discovered so far. We evaluated the predictabilityof machine learning models for the three-class classification task. The predicted quasicrystalline phases werecompared with known experimental phase diagrams,
and the precision and recall of the phase prediction
reached 0.793 and 0.714, respectively. We aim to use
this model to narrow down the candidate compositions that form quasicrystals and approximants.

■ Discovery of the laws of physics
Furthermore, by extracting the input-output rules
inherent in the black-box model of machine learning,
we have uncovered rule-of-thumbs on the formation ofquasicrystalline phases. These rules are expressed infive equations for the van der Waals radius of consistent elements and the itinerant valence electron concentration. These conditions provide design guidelines
for the search for new quasicrystals. This may open thedoor to a better understanding of the stability of quasicrystals, a central issue in condensed matter physics.

■ Towards discovery of innovative quasicrystals
With this work, we have taken the first step towardrealizing quasicrystal discovery through data science.
We are now using our model in the synthetization of
new quasicrystals. During nearly 35 years after the discovery of thefirst quasicrystal, little
is known about the formation mechanism of
quasicrystals. Data science may make a significant contribution tosolving this unsolved
issue in quasicrystal research.
Chang Liu

Figure:Formation rules on quasicrystalline phases thatwere discovered by machine learning.

9Research Center for Medical and Health Data Science

Pursuing the Advancement of Medical and Health Data Science■ Medical and health data science and thecenter’s mission
In recent years, expectations regarding data science
have been growing in various fields, including medical
and health sciences. These days, the application of deeplearning and other cutting-edge AI technologies to theanalysis of molecular/medical big data is widely expected to sharply accelerate the elucidation of biological anddisease mechanisms, the development of medical technologies, including drug discovery, and the realization of
precision medicine. In the meantime, we should not forget the critical role of obtaining solid evidence on medical technology from high-quality small data derived
through careful study design and statistical inference.
While it is considered that expectations regarding medical and health data science will continue to grow, theframework of data science has yet to be established to
fully meet such expectations; hence there is a big gap tobe filled. That is to say, the enhancement of education
and research in medical and health data science is oure-learning website

Symposium

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great challenge for the future.
The Research Center for Medical and Health Data Science was established in April 2018 based on researchand researcher education in data science and on the network of Japanese and foreign researchers that the Institute of Statistical Mathematics has established over many
years. The Center’s mission is to promote projects thatenhance education and research in medical and healthdata science in Japan. Although the Center has been in
existence for only three years, it has been involved in avariety of educational and research activities thus far.

■ Educational and research activities
In the area of education, the Center has been promoting the development of various educational programs,
such as systematic education courses on theories andmethods of statistical mathematics, biostatistics, theoretical epidemiology, and machine learning, topics thatform the basis of medical and health data science. In addition, the Center has focused on open lectures and onthe-job-training, as well as e-learning materials
to be shared with the research community.
In the research sector, the Center has been actively working on a variety of projects, such as
those involving statistical methodologies pertaining to advances in medical technology andhealthcare, as well as research in public healthand social medicine. Additionally, the Center isinvolved in big data analysis using cutting-edgemachine learning and AI algorithms, and is performing studies on foundational mathematics
and computer technologies.

■ Medical and Health Data Science
Research Network
All of the above projects are linked with theactivities of the newly launched “Medical and
Health Data Science Research Network.” This
unique network comprises 94 organizations (asof January 2022), including related academic societies, universities, and research institutions inall parts of Japan, as well as hospitals and companies. The substantial advancement of medical
and health data science depends on the organic
collaboration of researchers in the fields of statistical mathematics and information science andthose in the fields of medical and health science.
The Center intends to make every possible effort
to play an important part in bridging these fields.

Shigeyuki Matsui

Research Topics

A Confidence Interval Robust to Publication Bias
■ Meta-analysis in medical research■ Robust confidence interval

Meta-analysis is statistical analysis in which statistical results from some studies with a common purpose
are combined to obtain an overall result with higher
evidence. Typical examples of meta-analysis in medical
research are found in clinical trials to verify effects of
new drugs and epidemiological studies to explore causes of some diseases. For instance, in randomized clinical trials where a new drug and an existing drug arecompared, it is possible to obtain an overall result to
support the effect of the new drug, even if each trial
does not necessarily support it.

In meta-analysis, there are two types of statisticalmodels called fixed-effects model and random-effects
model, depending on whether we take into account theheterogeneity between studies. Although the randomeffects model is more flexible, it tends to be more affected by publication bias. We proposed to use thefixed-effects model even if there exists some heterogeneity and constructed a confidence interval which isrobust to publication bias, based on the fixed-effects
model with appropriate statistical adjustment (Henmi
and Copas, 2010). Figure 2 shows simulation results ofcoverage probabilities of some confidence intervals in
the presence of some publication bias. The horizontal
and vertical axes show the number of studies and thecoverage probabilities, respectively. The white circles
indicate the coverage probabilities of our proposal andtell us that our proposal keeps higher coverage probabilities than others. Our method is implemented in the
R-package called ‘metafor’ for meta-analysis (as a function ‘hc’) and is widely used in practice. An improved
version has been proposed recently for the case wherevery few studies are included in meta-analysis (Henmi,
Hattori and Friede, 2021).
Masayuki Henmi■ Publication bias
In meta-analysis, the data from each study tends tobe biased since it can be obtained only from publishedsources. For example, statistically significant results
tend to be published in clinical trials and epidemiological studies with two-group comparison, and therefore
the result of meta-analysis should be also statisticallysignificant if such published results are combined. This
sort of bias is called publication bias. Figure 1 shows
results of clinical trials to verify that some drug prevents premature birth. The horizontal and vertical axes
show estimates of log-odds ratios and reciprocals of
their standard errors from the trials. Since the standard errors are larger and the results do not tend to bestatistically significant in the bottom, the skewed plot
implies that there exists publication bias. This type ofplot is called a funnel plot, which is often used to checkexistence of publication bias.

1.0

0.8

3

4

0.6

2

0.4

1

0.2
5
-1.5

-1.0

Figure 1: Funnel plot of clinical trials.

-0.5

0.0

10

15

20

25

30

35

Figure 2: Coverage probabilities of some confidence
intervals.

11

Research Topics
Next-Generation Simulation NOE

Relativistic Gaussian Mixture Model■ Relativistic effects:
Particles travel slower than the speed of light
It has been known that the Gaussian mixture model isuseful for analyzing “non” relativistic plasma. When analyzing relativistic plasma, on the other hand, it is physically unreasonable to adopt a Gaussian distribution as acomponent distribution of a mixture model. This is because while the speed of plasma particles should be
slower than that of light due to the relativistic effects,
Gaussian distributions permit particles traveling fasterthan light. We need to take into account the relativistic
effects in the component distribution of the mixture
model.

■ Relativistic effects on Gaussian distribution
We have developed a mixture model composed of distribution that is obtained by applying Lorentz transformation to the plasma energy represented by the Gaussian distribution, which is known as the relativistic Maxwell
distribution or the Jüttner-Synge distribution. Here, wecall the component distribution the relativistic Gaussian
distribution. First, we summarize the basic characteristics of the relativistic Gaussian distribution (Table). The
table includes the normalization constants when thebulk velocity is non-zero, which has not been derived before. We also derived simple equations that are satisfied
by the maximum likelihood estimators of the parametersof the relativistic Gaussian distribution (bulk velocity
and temperature).

Table: Relativistic Gaussian distribution.

■ Relativistic Gaussian mixture model
We then proposed a relativistic Gaussian mixture model represented by the weighted sum of the relativistic
Gaussian distributions, and developed an EM algorithm
for estimating the parameters (mixing proportion, bulk
velocity and temperature of each component distribution). In particular, the M-step of the EM algorithm wederived equations whose solution is guaranteed to maximize the conditional expectation of the log-likelihood of
the complete data. To initialize the parameters for the EM
algorithm, we divide data into groups whose number corresponds to that of the components, and in each group
we use maximum-likelihood estimates or estimates by
the method of moments.

■ Application
We apply a two-component R-MMM to a distributionfunction by a particle-in-cell (PIC) simulation of relativistic pair plasma, and separate the simulated distributionfunction into two components (Figure). We find that one
component has a large bulk velocity while the other isalmost stagnant, and that the two components have almost the same temperatures, which is also consistent to
the initial temperature of the PIC simulation. Based on theparameters, we can infer large-scale plasma environments such shocks and discontinuities.

Figure: Application of a two-component relativistic
Gaussian mixture model.
References: Genta Ueno and Seiji Zenitani, “Relativistic Maxwellian
mixture model”, Physics of Plasmas 28, 122106 ( 2021)
https://doi.org/10.1063/5.0059126

Genta Ueno
12

Research Topics
Survey Science NOE

Introduction of the “Japanese National Character Study” Project■ What is the Japanese National Character Survey?
The Social Survey Research Group of the Institute of
Statistical Mathematics has conducted the Japanese National Character Survey (hereafter simply “National Character Survey”) once every five years since 1953, and its 14th
nationwide survey was carried out in the fall of 2018. Thissurvey has been conducted repeatedly using basically thesame survey method and content, and regarded as a representative example of “repeated social surveys” in Japan. It
has been widely used in research and education, as well as
in the mass media, to describe changes in values and attitudes in Japanese people of the postwar period.
The objectives of the National Character Survey are (1) toclarify the views and attitudes of the Japanese people andtheir changes through the survey results, (2) to research
new statistical survey methods that can respond to
future social changes through conducting the real-life
survey, and (3) to develop better statistical methods
for analyzing the survey data.

for the humane manager was 74%, the lowest ever, and support for the dry manager was 22%, the highest ever.

■ Future challenges
Because of its long history, the project itself has someproblems that could be considered a kind of exhaustion. For
example, the response rate has been declining in recent
years, and some items have seen little change in results. If
we change the content of survey items or survey methods,
it will be difficult to distinguish whether the Japanese people’s opinions have really changed or whether they havechanged only apparently due to changes in items or methods. The challenge for our group is how to refine the survey
methodology and capture new trends in Japanese opinion
within the limitations of a repeated survey.

Tadahiko Maeda#4. 10 Adopt a Child■ Long-term changes in Japanese opinion— major trends
Here is a typical example that captures changes in
attitudes over time. There are items that show a major trend in which “traditional views” favored by older generations have receded and newer views havegained support as time has passed (the # symbol below indicates the item number). For example, in theitem “#4.10 Adopt a child” the proportion of respondents who answered “No” increased when asked whether it is necessary to adopt other people’s children to
keep the family line when they have no children (Figure 1). This means that opinions about the traditionalfamily system have been replaced by new opinions
over the 65 years since the end of World War II.

Would
adopt
Would
not adopt
Depends
Other
D. K.

Figure 1: Increased opinion that the maintenance of the traditional familysystem (when there are no children, even someone else’s children should be adopted to keep the family line) is unnecessary.

#5.6 Type of boss prefered■ Recent trends in Japanese opinion— stability and fluctuation
Some items have been considered relatively stablein their results throughout time. For example, theJapanese tendency to “prefer humanistic answers”
has been considered relatively stable. However, in recent years, there seems to be a slight decline in thishumanistic preference. “#5.6 Types of boss preferred” (Figure 2) asks respondents to choose between “a manager who makes unreasonable requests
at work but also looks after you” (humane) and “amanager who does not make unreasonable requests
at work but never does anything for you personally”(dry). In the latest 14th nationwide survey, support

Never does
anything foryou personally
Looks after
you personally
Other
D. K.

Figure 2: The dry boss is gaining some support recently, whereas the humane boss has long been preferred.

13

NOE ( Network Of Excellence ) Project
Building a Framework for Advancing Strategic Research andPursuing a New Approach to Collaborative Research■ Research and Educational Activities asa Biaxial Structure

The Institute of Statistical Mathematics (ISM) pursues research and education in the own biaxial structure; one is structured by three basic research departments and the other is by four NOE-type research
centers and two organizations for professional development. By its nature, the basic research departments
cut across and link various disciplines, with the goal ofdeveloping tools for interdisciplinary research. The
field of statistical and mathematical sciences must itself evolve to meet the changing needs of society andthe data environment, and is therefore constantly
evolving as a field of study. At the same time, there areapproaches and directions that have remained unchanged as the field evolves. There are three basic research departments: Statistical Modeling, Statistical
Data Science, and Statistical Inference and Mathematics. Almost all tenured research staff at ISM are assigned in principle to one of these basic research departments. These departments engage in cutting edge
research aimed at developing methodologies for rational prediction and decision making, based on data andexisting knowledge. We regularly assess whether our
research system is functioning effectively from theviewpoints of research trends and prospects in statistical and mathematical sciences.
On the other hand, NOE-type research centers andorganizations for professional development (along thevertical axis) are staffed by permanent researchers
within ISM, project professors/researchers (post-doctoral researchers), and visiting professors and researchers. There are four NOE-type research centers:
the Risk Analysis Research Center, the Research Center for Statistical Machine Learning, the Data Science
Center for Creative Design and Manufacturing, and the
Research Center for Medical and Health Data Science.
These centers conduct research activities at the interface of statistical and mathematical sciences and individual scientific disciplines to find solutions to urgent
social problems. In addition, two organizations arecommitted to professional development. The School of
Statistical Thinking, which was established in 2011, isthe base for the Project for Fostering and Promoting
Statistical Thinking and provides various programs.
The Center for Training Professors in Statistics was established on January 1, 2022 to promote the Project forTraining Experts in Statistical Sciences. This center
also trains statistics professors and experts in statistical sciences, primarily through graduate school education. For more details, please see each project’s webpage.

■ NOE ProjectIn accordance with the second mid-term plan of the
Research Organization of Information and Systems* including Risk Research Network

Period of the First mid-termplan of ROIS ( 2004-2009 )Beginning** included in the Risk Analysis Research Center

Period of the Second mid-term plan of ROIS

Period of the Third mid-term plan of ROIS

Promoting

Developing( 2010-2015 )Forming of Risk
Research Network

Risk Research
NOE *Risk Analysis
Research Center

Risk Analysis
Research Center

Est.: Apr., 2005

Reorganized: Jan., 2012

ISM

Next-Generation
Simulation NOE
Research andDevelopment Center
for Data Assimilation
Est.: Jan., 2011
Closed: Mar., 2019

Survey Science
NOE
Survey Science
Center( 2016-2021 )Medical and Health
Data Science NOE
Research Center for Medicaland Health Data Science

Statistical Machine
Learning NOE
Research Center for
Statistical Machine
Learning

Est.: Apr., 2018

Est.: Jan., 2012

Data Science forCreative Design and
Manufacturing NOE
Data Science Centerfor Creative Designand Manufacturing

Service Science
NOE
Service Science
Research Center
Est.: Jan., 2012
Closed: Jan., 2017

Developmental
integration

Data Science NOE for
Resource-Environment
Management
Data Science
Research Unit for
Resource-Environment
Management **Est.: Apr., 2020

Est.: Jul., 2017

Est.: Jan., 2011
Closed: Jan., 2017

ROIS

Joint-Support Center for Data Science Research, ROIS

Fig. 1: Brief history of NOE Project ( dotted lines mean history or transformation in each NOE ).

14

ISM Projects(ROIS, ISM’s parent organization), ISM had set as a goal
the establishment of NOE in statistical mathematics.
Initially, ISM established NOE in Risk Research, NextGeneration Simulation, Survey Science, Statistical Machine Learning, and Service Science. Over the next
few years, however, ISM reorganized NOE domains or
NOE-type research centers considering the needs ofeach community as well as modern society as a whole(Fig.1).
To fulfill the goal of establishing new scientific methodologies in a knowledge-based society, in which theimportance of knowledge goes beyond merely solving
individual problems, the NOE activity is being systematically pursued under the unified guidelines formulated by Managing Committee of the NOE Project. We
also commission experts from the industrial, academic, and government sectors to be members of the Advisory Board of NOE Project, and their advice helps us topromote the project much more effectively.
After completing the reorganization, which began in
FY 2016, the 3rd Advisory Board Meeting of NOE Project was held on December 17, 2019. All six members of
the board attended the meeting. They represented
each NOE domain and engaged in discussion with core
members of the project at ISM. The meeting provided
an opportunity to collect valuable opinions about our
future plans. The Advisory Board was restructured in
FY 2021, and it consists of experts in each NOE domain,
including newly appointed members (Fig. 2).

Each year, the number of Memoranda of Understanding (MOUs) with research organizations within
Japan and overseas, which are conducted by NOEtype research centers, is increasing. In particular, academic exchanges encompassing multiple NOE domains provide significant opportunities to integratedifferent research fields and create new ones. ISM provides flexibility for research and contributes to society. Consequently, ISM’s general research in statisticaland mathematical sciences is highly demanded by various research fields in both the humanities and sciences. Recent reorganizations at ISM have deepened andpromoted the NOE Project.
In the fourth mid-term plan for six years of ROIS,
which began in April 2022 (and continues until March
2028), ISM set the following goals: (1) to promote thedevelopment of cutting-edge mathematical techniques
and industry-government-academia collaborations, (2)reorganize NOE-type research centers so that they aregoal oriented and classified by both methodology typeand domain type, and (3) enhance relations with otherresearch organizations and increase NOE-type MOUs.
ISM is continuously expanding and developing thisNOE project with the aim of establishing new scientificmethodologies (“Fourth Paradigm: Data Science”), creating new research disciplines, and developing newstyles of joint research. For up-to-date information,please visit the website. We truly appreciate your understanding and support for this project.

https://www.ism.ac.jp/noe/project/en/

Data Science NOE for
Resource-Environment
Developmental
Management
integration

Risk Research
NOE

Risk Research
Network

RelatedOrganizations

RelatedOrganizations

Data Science Research Unit
for Resource-Environment
Management*

Risk Analysis
Research Center

Medical and Health
Data Science NOE

IS M
IS

*included in the Risk
Analysis Research Center

Next-Generation
Simulation NOE
Data Assimilation
Group, Dept. of
Statistical ModelingResearch Center for
Medical and Health
Data Science
Managing Committeeof NOE Project
NOE Promotion
Unit

Medical and Health
Data Science
Research Network

Advice from
Advisory Board
Relations in ISM
Relations by MOUs

Advisory Board of
NOE Project

RelatedOrganizations

Survey Science Group,
Dept. of Statistical
Data Science

Data Science Centerfor Creative Designand Manufacturing
RelatedOrganizations

Data Science forCreative Design and
Manufacturing NOE

Research Center
for Statistical
Machine Learning
RelatedOrganizations

RelatedOrganizations

Survey Science
NOE

Statistical Machine
Learning NOE

Fig. 2: Relationship diagram of NOE Project.

15

Project for Fostering and Promoting Statistical Thinking

Rapid development of information and communication technology has led to the explosion of data. Now surrounded by “Big Data”, everybody is expected to “think statistically”. More than ever, there is a need for data
scientists who can handle such big data and are able to extract useful knowledge from it. Meanwhile, Japanesehigher education is exhibiting a deplorable lack of production capacity in terms of data scientists. This can be accounted for by the fact that, until quite recently, no academic institution other than ISM had a Ph. D. course in statistics and the small number of statisticians in academia are isolated from each other, being scattered over various disciplines. Hoping to gain a little traction on this problem, ISM established the School of Statistical Thinking,into which we integrated all of our educational resources. In FY 2016, ISM established the Managing Committeeof School of Statistical Thinking, inviting contributions from outside experts, and in FY 2017 we launched the
Leading DAT program by adopting the suggestions by the committee. In FY 2020, we launched online courses.
The following are the principal projects.

Research Collaboration Startup
The Institute had already been providing a consultation service for statistical science, but along withthe launch of the School of Statistical Thinking inNovember 2011, this service was reorganized as a
research collaboration startup. This program, being
one of the projects to foster and promote statisticalthinking, is mainly aimed at supporting applied scientists and other non-experts. Expert statisticians
affiliated with the Institute give them advice on statistical modeling, data analysis, and research. Somecases have developed into official research collaborations, which are our primary duty as an inter-university research institute. The Institute accepts around25 cases annually, some of which benefit society indiverse ways.

Data Science Research Plaza
Researchers funded by private-sector firms canmaintain a desk and phone in the School of Statistical Thinking. This program is subject to fees, and thecontract can be renewed annually. A faculty mentor
gives advice to the accepted funded researcher sothat he or she can freely attend various events, suchas seminars, workshops, conferences, and extension
courses. After learning the expertise of the ISM research staff, participants in this program are invited
to take advantage of paid consultations and funded
research collaboration.

Open-type Professional Development ProgramThis is a spin-out program from ISM cooperative
research projects. Establishing a goal is an indispensable element of the proposal of a cooperative research project. On the other hand, such goal setting
is irrelevant for a summer school program, study session, or retreat. Since the launch of the School of Statistical Thinking, organizers of such group-oriented
16

study programs can apply to the Open-type Professional Development Program. There are two categories under this program: one is ‘workshop’ and theother is ‘intensive training for young researchers’. For
FY 2021, eight workshops have been accepted after
review.

ISM Projects

Statistical Mathematics Seminar Series
The Institute holds weekly seminar series on statistical mathematics every Wednesday. The seminars
are led by in-house and external lecturers to showcase their latest studies. These lectures are currently held online due to COVID-19. To view the seminarschedule and learn more about the program, please
visit the Institute of Statistical Mathematics website.
https://www.ism.ac.jp/index_e.html

Leading DATIn FY 2017, the School of Statistical Thinking
launch­ed a program called “Leading DAT” aimed attraining data scientists with the knowledge and skills
in statistical mathematics required by modern society. In FY 2021, we held three Leading DAT
lectures online, entitled “L-A. Introductory Data Science”, “L-B1. An Introduction to Statistical Modeling” and “L-B2.
Machine Learning and Modern Methodologies in Data Science”. Around half of
the attendants of L-B1 and L-B2 enroll the
Leading DAT Training Course, in which
we grant certificates to participants who

have fulfilled the course requirements, including attendance in L-B1 and L-B2 lectures and submissionof reports. A total of 21 people have been granted thecertificate of completion.

Online lecture ( L-B1)

Tutorial Courses
The education program at ISM dates back to 1944,
the year of founding. The Ministry of Education installed a training center within ISM to foster technicians in numerical computation. After the World War
II, this training center was relaunched in 1947 to develop pollsters and census takers. It helped to cultivate professionals in the field of statistical surveys,
while a growing number of entries from business andindustry coming for various types of training werealso observed.
Now the tutorial courses are operated by the

School of Statistical Thinking, which was establishedin 2011.
In FY 2021, 6 lectures and one course (including
Leading DAT) were held and the number of participants was 664. The total numbers of lectures andcourses held from 1969 to March, 2022 were 392 and5 respectively, with a total of 28,884 participants.
These lectures covered a wide range of fields frombasic to applied statistics. The schedule of tutorial
courses can be found on the website of the Instituteof Statistical Mathe­matics.
https://www.ism.ac.jp/lectures/kouza.html
17

Research Cooperation

International Cooperation■ Associated Foreign Research Institutes

18

Organization name

Address

Conclusion date

The Statistical Research Division of the U.S. Bureau of the Census

USA ( Washington )July 27, 1988

Stichting Mathematisch Centrum

The Kingdom of the Netherlands( Amsterdam )May 10, 1989

Institute for Statistics and Econometrics, Humboldt University of Berlin

Germany ( Berlin )December 8, 2004

The Steklov Mathematical InstituteRussia ( Moscow )August 9, 2005

Central South University

China ( Changsha )November 18, 2005

Soongsil University

The Republic of Korea ( Seoul )April 27, 2006

University of Warwick

The United Kingdom ( Coventry )January 16, 2007

Indian Statistical Institute

India ( Kolkata )October 11, 2007

Institute of Statistical Science, Academia Sinica

Taiwan ( Taipei )June 19, 2008

Department of Empirical Inference, Max Planck Institute for Biological Cybernetics

Germany ( Tubingen )August 11, 2010

Department of Communication Systems,
SINTEF Information and Communication Technology

Norway ( Trondheim )January 30, 2012

University College London

The United Kingdom ( London )February 16, 2012

Department of Electronics and Telecommunications,
Norwegian University of Science and Technology

Norway ( Trondheim )May 22, 2012

Department of Probability and Mathematical Statistics, Charles University in Prague

Czech Republic (Prague )October 10, 2012

Department of Ecoinformatics, Biometrics and Forest Growth of the
Georg-August University of Goettingen

Germany (Goettingen )October 18, 2012

Korean Statistical Society (KSS )The Republic of Korea ( Seoul )July 9, 2013

Toyota Technological Institute at Chicago

USA ( Chicago )February 10, 2014

Australian National University

Australia ( Canberra )May 15, 2014

RiskLab ETH Zurich

Switzerland ( Zurich )February 7, 2015

Institut de Recherche en Composants logiciel et matériel pourl’Information et la Communication Avancee

France ( Paris )February 9, 2015

Centre de Rechereche en Informatique, Signal et Automatique de Lille

France ( Paris )February 12, 2015

University College London Big Data Institute

The United Kingdom ( London )February 26, 2015

The Institute of Forestry, Pokhara of Tribhuvan University

Nepal ( Pokhara )March 6, 2015

The Institute of Forest and Wildlife Research and Developmentof the Forestry Administration of Cambodia

Cambodia ( Phnom Penh )March 6, 2015

The Chancellor masters and Scholars of the University of Oxford

The United Kingdom ( Oxford )March 10, 2015

Forest Inventory and Planning Institute of Vietnam

Vietnam ( Hanoi )June 2, 2015

Zuse Institute Berlin

Germany (Berlin )June 20, 2016

The University of Porto

Portugal ( Porto )June 22, 2016

National University of Laos

Laos ( Vientiane )March 15, 2017

Institute of Geophysics China Earthquake Administration

China ( Beijing )April 28, 2017

Hong Kong Baptist University

Hong Kong ( Kowloon Tong )August 28, 2017

University of Malaya

Malaysia ( Kuala Lumpur)

September 18, 2017

Unversidade de Évola

Portugal ( Evola )November 30, 2017

Universität Ulm

Germany ( Ulm )December 8, 2017

The Korean Association for Survey Research

The Republic of Korea ( Seoul )February 14, 2018

The Jean Golding Institute for data-intensive research, University of Bristol

The United Kingdom ( Bristol)January 15, 2019

Survey Research Center, Sungkyunkwan University

The Republic of Korea ( Seoul )February 25, 2019

University of LampungIndonesia ( Lampung )March 6, 2019

Department of Earth and Space Sciences,
Southern University of Science and Technology

China ( Shenzhen Shi )March 25, 2019

Université Bretagne Sud

France ( Lorient )March 29, 2019

North Carolina State University

USA ( Raleigh )November 13, 2019

National University of Singapore
Konrad-Zuse-Zentrum für Informationstechnik Berlin

Singapore
Germany ( Berlin)

February 25, 2020

International Cooperation
Associated Foreign Research Institutes
Organization name

Address

Conclusion date

Singapore-ETH Centre

Singapore

March 18, 2020

Department of Actuarial Studies and Business Analytics, Macquarie University

Australia (Sydney)

December 21, 2020

EURECOM

France (Sophia Antipolis)

August 25, 2021

The University of Texas at Dallas,School of Economic, Political and Policy Sciences

USA (Dallas )March 11, 2022

* There are two more agreements concluded.

Research Collaboration
ISM performs many activities for collaborating with researchers in the various fields of statistical science, fromthe individual level to the national level. The ISM cooperative research program regularly performs research activities to provide the research resources of ISM to researchers at universities or research institutes in order toadvance their academic research. Available research resources include books, journals, supercomputers, somecommercial statistical software packages, as well as statistical packages developed by ISM, and also the researchers in ISM themselves, who have abundant professional knowledge and experience in statistical science and data
analysis. The ISM cooperative research program provides not only research support funds but also opportunities
for the various researchers in many fields who require statistical knowledge to make use of the resources available at ISM. ISM’s aim is to be a place for interaction and fusion among researchers inside and outside of ISM, andto contribute to multidisciplinary development of both the theory and the application of statistical science.

■ Number of Activities
Year

2016

2017

2018

2019

2020

2021

Number of Activities

187

161

166

178

145

143

■ Fields of Research Collaboration
Research collaboration is classified by research field as follows. Applicants can use the table below to find themost appropriate type of project.
ISM Fields
NumberFieldsa

Prediction and Control Groupb

Complex System ModelingGroup

Major Research Fields
NumberFieldsMajor Research Domains

1

Statisticalmathematics

Mathematical theory of statistics, optimization, etc.

2Information science

Algorithms, use of computer in statistics, etc.

c

Data Assimilation Group3

Biological science

Medicine, pharmacy, epidemiology, genetics, etc.

dSurvey Science Group4

Physical science

Space, planet, earth, polar region, materials, etc.

eMetric Science Group

5

Engineeringf

Structure Exploration Group

Mechanics, electronics, control, chemistry,architecture, etc.

g

Mathematical Statistics Group

6

Human science

Philosophy, art, psychology, education,
history, geography, culture, language, etc.

hLearning and Inference Group7

Social science

Economics, law, politics, society, manage­ment,
official statistics, population, etc.

i

Mathematical Optimization
Group
8

Environmental
science

Environmental statistics, environmentrics,
agricultural statistics, statistical meteorology,
land economics, landscape management,
forest management, etc.

9

OthersOther research fields

jOthers19

Graduate School Program

Organization
The Institute of Statistical Mathematics is one of the platforms of SOKENDAI (The Graduate University for
Advanced Studies; the headquarters in Hayama, Kanagawa), which was opened in October 1988 to offer graduate education. Since its opening, the Institute has included the Department of Statistical Science and, sinceApril 1989, has accepted students for education and research in doctoral programs. In 2006, the Instituteadopted a five-year system, offering either a five-year education and research program, or a three-year education and research program starting from the third year of study.
Regional Studies
Comparative Studies

School of Cultural and Social Studies

Japanese Studies
Japanese History
Japanese Literature
Structural Molecular Science
Functional Molecular Science

School of Physical Sciences

Astronomical Science
Fusion Science
Space and Astronautical Science

SOKENDAI

Accelerator Science

School of High Energy Accelerator Science

Materials Structure Science
Particle and Nuclear Physics
Statistical Science

School of Multidisciplinary Sciences

Polar Science
Informatics
Genetics

School of Life Science

Basic Biology
Physiological Sciences

School of Advanced Sciences

Evolutionary Studies of Biosystems

Outline of Education and Research
The Department of Statistical Science, which is based on the Institute of Statistical Mathematics (ISM) serving
as its underlying platform, aims to cultivate individuals who possess creative research skills to contribute tosolving various important intricately-intertwined problems. To this end, the Department conducts education
and research related to the basis, mathematics and applications of data collection designs, modeling, inference
and prediction, and equip students with the ability to extract information and knowledge from the real world
based on the effective use of data.
Field of Education and Research

20

Contents

Statistical Modeling

We provide education and research focused on dynamic modeling such as spatial and space-time modeling, graphical modeling of temporally and/or spatially inter-related complex phenomena, and intelligent
information processing. We also provide education and research on statistical inference based on various
models, methods of calculation for inference, and evaluation of models based on data.

Statistical Data Science

We provide education and research on data design, investigation and analytical methods to cope with theuncertainty and incompleteness of information, as well as on computational statistics.

Statistical Inference
and Mathematics

We provide education and research on the theory of statistics and related basic mathematics, statistical learningtheory to extract information from data by automated learning and inference techniques; as well as theory andapplications of optimization and computation algorithms which serve as the basis for computational inference.

Features of Education and Research● The Department of Statistical Science is one of the few comprehensive doctoral programs in statistical sci-ence in Japan, and has accepted students from a wide range of academic fields. Education and research in all
aspects of statistical science is conducted by faculty members specializing in a variety of fields, from theory
to application.

● The Institute of Statistical Mathematics, the platform for the course, is equipped with a world-class supercomputer, high-speed 3D graphic computers and simulators to generate physical random numbers, as well as
a variety of software, including original statistical software developed by the Institute.

● The Institute of Statistical Mathematics has an extensive library covering a wide variety of journals and books

on statistical and mathematical sciences.

● The Institute of Statistical Mathematics, as a joint research institute, frequently holds research meetings andseminars presented by visiting professors and researchers from Japan and abroad. Students are encouraged
to participate in these seminars and interact with the presenters.

● Students have the opportunity to participate in different research projects through collaborations with otheruniversities and research institutes.

Course Requirements and Type of Degree Granted● Requirements to complete the doctoral course are as follows:

Completion of at least 40 credits, including the required ones, by a student in the five-year program, or completion of at least 10 credits by a three-year doctorate student who previously completed a Master’s course;
meeting all the criteria set by the thesis committee of the Institute; and successfully completing the examination.

● On completion of the course, either a Doctorate in Statistical Science or, if the thesis deals mainly with aninter-disciplinary field related to statistical science, a Doctorate of Philosophy is awarded.

● The required number of years of study will be flexible if a student demonstrates outstanding research results.

Number of Students ( As of April 1, 2022 )■ 5-year doctoral

course:Quota,2■ 3-year doctoral

course:Quota,3Year of enrollment

2018

2019

2021

2022

Number of students

1

1

1

1

Year of enrollment

2016

2017

2018

2019

2020

2021

2022

Number of students

1①

1①

5⑤

9⑦

6③

5③

2②

* The figures in circles indicate those who are employed by other organizations.

University Background of Students
National and public universities• Hokkaido University ( 5 ) • Tohoku University (5 ) • Fukushima University (1 ) • University of Tsukuba (7 ) • Saitama University (1 )• Chiba University (1) • Ochanomizu University (1) • National Graduate Institute for Policy Studies (1) • Tokyo Medical and Dental
University (1) • Tokyo University of Marine Science and Technology (1) • Tokyo Gakugei University (2) • Tokyo Institute of Technology ( 6 )• The University of Tokyo (24 ) • Tokyo Metropolitan University (1) • Tokyo University of Agriculture and Technology (1) • Hitotsubashi
University (6 ) • Shizuoka University (1) • Kanazawa University (1) • Japan Advanced Institute of Science and Technology (1) • Nagoya
University (4 ) • Toyohashi University of Technology ( 2 ) • Kyoto University ( 8 ) • Osaka City University (1) • Osaka University ( 3 )• Nara Institute of Science and Technology (1) • Okayama University ( 2 ) • Shimane University (3 ) • Kyushu University ( 4 ) • Oita
University (1) • The University of Electro-Communications ( 1)21Graduate School Program

University Background of Students
Private universities• Aoyama Gakuin University (1 ) • Kitasato University (1 ) • Keio University (8 ) • International Christian University (1) • Shibaura Institute ofTechnology (1 ) • Sophia University (1 ) • Chuo University (9 ) • Tokyo University of Science (7 ) • Toyo University (1) • Japan Women’s
University (1 ) • Nihon University ( 2 ) • Hosei University (7 ) • Waseda University (9 ) • Nanzan University (1 ) • Osaka Electro-Communication
University ( 1 ) • Kansai University ( 1 ) • Kyoto Sangyo University (1 ) • Ritsumeikan University (1 ) • Okayama University of Science (1 )• Kurume University (1 )Foreign universities• Aston University (1) • University of California, Irvine (1) • California State University, Long Beach (1) • University of Campinas (1)• University of Colorado Boulder (2) • University of Dhaka (2 ) • University of Hawaii (1) • Jahangirnagar University (2 ) • University of
Malaya (1) • Northeast Normal University (1) • Ohio University ( 2) • University of Rajshahi (2 ) • Stanford University (1) • The University of
Nottingham (1 ) • Zhejiang University (1 ) • Institute of Applied Mathematics, AMSS, CAS (1 ) • University of Science and Technology ofChina (1 ) • Center for Analysis and Prediction, China Seismological Bureau (1 ) • Northeastern University (1 ) • The Hong Kong Universityof Science and Technology (1 ) • China University of Geosciences (1 )Degrees AwardedYear
Doctor of Philosophy

2015

2016

2017

2018

2019

2020

2021

5

7

5

5

5

4

7

AlumniNational and public universities, and public organizations• Obihiro University of Agriculture and Veterinary Medicine • University of Tsukuba • University of Hyogo • The University of Tokyo• The University of Electro-Communications • Saitama University • Nagoya University • Kyushu University • Kyushu Institute of Technology• University of the Ryukyus • The Institute of Statistical Mathematics • Tohoku University • Yokohama National University • Hokkaido
University • Tokyo Institute of Technology • Hiroshima University • Oita University of Nursing and Health Sciences • JAXA’s Engineering
Digital Innovation Center • Kyoto University • Nara Institute of Science and Technology • Bank of Japan • Japan Broadcasting Corporation• Railway Technical Research Institute • Statistical Information Institute for Consulting and Analysis • Government Pension Investment Fund• Public School • RIKEN • Statistics Bureau of Japan • Pharmaceuticals and Medical Devices Agency (PMDA)
Private universities• Sapporo Gakuin University • Tokyo Health Care University • Meiji University • Doshisha University • Josai University • Nihon University• Komazawa University • Aichi University of Technology • Tokyo University of Information Sciences • Shibaura Institute of Technology• Rikkyo University • Waseda University • Keio University • Tokyo Medical University
Foreign universities• Jahangirnagar University • Victoria University • Massey University • University of Otaga • Statistics New Zealand • University of
Rajshahi • University of California, Los Angeles • Asia-Pacific Center for Security Studies Department • Central South University• Hong Kong Baptist University • University of South Carolina • The University of Warwick
Private companies, etc.
• Hitachi, Ltd. Central Research Laboratory • NTT Communication Science Laboratories • Seiwa Kikaku • NLI Research Institute• Mizuho Trust and Banking • Nomura Securities Co., Ltd. • ATR Computational Neuroscience Laboratories • Toyota Motor Corporation,
Higashi-Fuji Technical Center • Schlumberger Limited • Macquarie Securities, Japan • Non-Life Insurance Rating Organization of Japan• Barclays Global Investors • Open Technologies Corporation • Yamaha Corporation • Goldman Sachs Asset Management L.P. • CLC bio
Japan, Inc. • MUFG Bank, Ltd. • Pfizer Japan Inc. • Doctoral Institute for Evidence Based Policy • Sony Corporation • NTTIT Corporation• Sompo Japan Insurance Inc. • Qualicaps Co.,Ltd. • Bridgestone Corporation • Brain Pad Inc. • Sumitomo Chemical Co.,Ltd.
• PricewaterhouseCoopers Aarata • Mitsubishi Tanabe Pharma Corporation • Daiichi Sankyo Co.,Ltd. • Shizuoka Cancer Center • CPC
Clinical Trial Hospital, Medipolis Medical Research Institute • CRD Association • Japan Society for the Promotion of Science • Tokyo
Electric Power Company Holdings, Inc. • Asahi Kasei Corporation • Honda R&D Co.,Ltd. • Yokogawa Electric Corporation • Kao
Corporation • Advanced Smart Mobility Co., Ltd. • NEC Corporation • Janssen Pharmaceutical K.K. • Taisho Pharmaceutical Holdings• Otsuka Pharmaceutical Co., Ltd. • Kyowa Kirin Co., Ltd. • KOSÉ Corporation • Novartispharma K.K.

22

Facilities and Equipment

Computational Resources ( As of April 1, 2022)
ISM is operating a supercomputer system, the Supercomputer System for Statistical Science, which is suitable for analyzing large-scale datasets. The Supercomputer System for Statistical Science, an HPE SGI 8600 system,has been operated since October 2018. The system is a
distributed-memory parallel computer that has total theoretical peak performance of 1.49 PFLOPS. The system isliquid cooled and consists of 384 computing nodes. Eachnode has two CPU chips (Intel Xeon GOLD 6154) with 18
cores and has 384 GB memory. The system includes hardware random number generator. For visual representation of research results, ISM has a 200-inch wide screen
and a projector capable of showing 3D movies in 4K resolution in the historical computers exhibit room.
In March 2021, we introduced the Communal Cloud
Computing System to provide a computing environmentthat is easy for each user to use and customize. This sys-

tem is equipped with 64 computing nodes (HPE ProLiant
DL385 Gen 10 Plus; total theoretical computing performance of 154.0 TFLOPS), and each node has two 32-coreCPUs (AMD EPYC 7452), 1 TB of main memory, and an
SSD with 20 TB of usable capacity.
In the main office building, the primary local area
network (LAN) consists of an Ethernet network using
10GBase-SR for the main trunk and 1000Base-T for branches. The personal computers in researchers’ offices and thesupercomputer system are all connected to this network.
A wireless LAN system is also available in the immediatearea of the building occupied by ISM. These LAN systems
enable distributed processing and allow computing resources and statistical data to be used effectively. Comprehensive network security methods have been implemented, such as a firewall system, anti-virus software,and an intrusion prevention system. To encourage joint
research with researchers both in Japan and abroad, as
well as the exchange of e-mails, the network is connectedto the Internet through SINET6 (100Gbps).

Supercomputer System for Statistical Science ( HPE SGI 8600 )

200-inch wide screen showing 3D movies in 4K resolution

Library and Materials ( As of April 1, 2022)
We have a large number of major Japanese/foreign
journals covering a wide variety of fields including statistics, mathematics, computer science and informatics. In
addition, we also have a large library consisting of books
on humanities, social science, biology, medical science,science and engineering.
Besides contributed to Japanese and foreign pub­lications, we also have a collection of journals that wepublish ourselves: Annals of the Institute of Sta­tistical
Mathematics (English; Springer), Proceedings of the Institute of Statistical Mathematics (Japanese), ISM Survey
Research Report (Statistical Researches mainly related
to the Japanese National Character), Computer Science
Monographs, Cooperative Research Re­ports (for collaborative research projects), Research Memorandum, ISM

Reports on Statistical Computing, and ISM Report on
Research and Education.
All materials are properly catalogued and can besearched from the web in order to meet the needs ofresearch­ers working in a wide of fields. We also accept
photocopy requests.

Japanesebooks
20,64128%

Books
73,985

Japanese
periodicals
1,157
53%

Foreignperiodicals

Periodicals
2,202

1,045
47%

Foreign books
53,34472%

23

Finance and Buildings

Administration Subsidy and Others ( 2021)
Type
Expenditure

Personnel expenses

Non-personnel expenses

Total

675,641

975,623

1,651,264
Unit: 1,000JPY

Accepted External Funds ( 2021)
Type
Joint research
Joint research division
Subcontracted research, Trustee businessContract researchers
Academic consulting
Contribution for scholarship
Total

Items

Income

19
2
24
1
5
3

29,89821,700509,860
284
4,789
1,580

54

568,111
Unit: 1,000JPY

Grants-in-Aid for Scientific Research “ KAKENHI” ( 2021)
Research Category
Grant-in-Aid for Scientific Research on Innovative Areas
Grant-in-Aid for Transformative Research Areas ( A )Grant-in-Aid for Transformative Research Areas ( B )Grant-in-Aid for Scientific Research ( S )Grant-in-Aid for Scientific Research ( A )Grant-in-Aid for Scientific Research ( B )Grant-in-Aid for Scientific Research (C)Grant-in-Aid for Challenging Research ( Exploratory)
Grant-in-Aid for Challenging Research ( Pioneering )Grant-in-Aid for Early-Career Scientists
Grant-in-Aid for Forming an Independent Foundation
Grant-in-Aid for Research Activity Start-up
Grant-in-Aid for JSPS Fellows
Total

Items

Amount Granted

3
—
1
—
5
7
17

24,050—9,880—
39,78025,87017,810

2
1
14
1
—
1

2,860
9,100
17,550
1,820
—
780

52

149,500
Unit: 1,000JPY

Site and Buildings ( As of April 1, 2022)

24

Site Area

62,450m 2

Area for Buildings ( total )16,209m 2

Organization

Organization Diagram ( As of April 1, 2022)
International
Advisory Board

Department of Statistical
Modeling

Council

Department of Statistical
Data Science

Cooperative ResearchCommittee
Advisory Board of
NOE Project

Department of Statistical
Inference and Mathematics■ Prediction and Control Group■ Complex System Modeling Group■ Data Assimilation Group■ Survey Science Group■ Metric Science Group■ Structure Exploration Group■ Mathematical Statistics Group■ Learning and Inference Group■ Mathematical Optimization Group

Risk Analysis Research Center

Managing Committee of
School of Statistical Thinking

Research Center for
Statistical Machine Learning

Research Ethics Board

Data Science Center for Creative
Design and Manufacturing
Research Center for Medical and
Health Data Science

DirectorGeneral

School of Statistical Thinking

Vice DirectorGeneral

Center for Training Professors
in Statistics
Center for Engineering andTechnical Support
Library
Planning Unit

■ Computing Facilities Unit
■ Computer Networking Unit
■ Information Resources Unit
■ Media Development Unit

■ URA Station

Evaluation Unit
Information and Public Relations Unit
Administration Planningand Coordination Section

Industry-Academia Collaboration and Intellectual Property Unit
NOE Promotion Unit
Gender Equality Unit
International Affairs Unit
General Affairs Division
Financial Division

Tachikawa Administration
Department

Accounting Division
Research Promotion Division
NIPR Planning and Coordination Division
ISM Planning and Coordination Division25

Number of Staff ( As of April 1, 2022)
DirectorGeneral

Type
Director-General

Professor

Associate
Professor

5

6

Assistant
Professor

Administ­
rative Staff

Technical
Staff

Total

1

1

Department of Statistical Modeling
Department of Statistical Data Science

56Department of Statistical Inference and Mathematics

7

8

School of Statistical Thinking

11
3

14
15

5

5

Center for Engineering and Technical Support

10

Administration Planning and Coordination Section
Tachikawa Administration Department
Total

1

17

20
(

8

10

1

1

( 37)

( 37)

1 ( 37)10

57( 37 )) Total number of staff of Tachikawa Administration Department.

The number under Technical Staff at the Center for Engineering and Technical Support include
two staff members who retired because of age and are reemployed in a different position.

Staff ( As of August 1, 2022)
Director-General

Hiroe TSUBAKI

Vice Director-General

Yoshinori KAWASAKI

Vice Director-General

Satoshi YAMASHITA

Department of Statistical Modeling
Director

Tomoko MATSUI• Prediction and Control Group ————————————————————————————————————————————————————————————
Prof.
Assoc. Prof.

Yoshinori KAWASAKI
Jiancang ZHUANGProf.
Assoc. Prof.

Atsushi YOSHIMOTO
Yumi TAKIZAWA

Assoc. Prof.

Fumikazu MIWAKEICHI• Complex System Modeling Group ——————————————————————————————————————————————————————————
Prof.
Assoc. Prof.

Tomoko MATSUI
Shinsuke KOYAMAProf.
Assoc. Prof.

Yukito IBA
Kengo KAMATANI

Prof.

Hideitsu HINO

• Data Assimilation Group ———————————————————————————————————————————————————————————————

Genta UENO
Yusaku OHKUBO
Visiting Prof.
Tadahiko SATO
Visiting Assoc. Prof. Hiroshi KATO
Visiting Assoc. Prof. Shunichi NOMURA

Shigeru FUJITA
Masako KAMIYAMA
Visiting Prof.
Kazuyuki NAKAMURA
Visiting Assoc. Prof. Masaya SAITO
Visiting Assoc. Prof. Yosuke FUJII

Shinya NAKANO
Toshikazu KITANO
Visiting Prof.
Tomoyuki HIGUCHI
Visiting Assoc. Prof. Hiromichi NAGAO
Visiting Assoc. Prof. Takashi YAMAMOTO

Prof.

Project. Prof.

Assoc. Prof.

Project Researcher

Visiting Prof.

Visiting Prof.

Department of Statistical Data Science
Director

Kazuhiro MINAMI• Survey Science Group —————————————————————————————————————————————————————————————————————
Prof.

Kazuhiro MINAMI

Assoc. Prof.

Tadahiko MAEDA

Assoc. Prof.

Yoo Sung PARK

Project Assoc. Prof. Naoko KATO

Project Assist. Prof. Kiyohisa SHIBAI

Project Assist. Prof. Le Duc ANH

Visiting Prof.

Takatoshi IMADA
Wataru MATSUMOTO
Visiting Assoc. Prof. Koken OZAKI

Visiting Prof.

Toru KIKKAWA
Kazufumi MANABE
Visiting Assoc. Prof. Taisuke FUJITA

Visiting Prof.

Visiting Prof.

Visiting Prof.

Visiting Assoc. Prof. Yusuke INAGAKI

Yoshimichi SATO

• Metric Science Group —————————————————————————————————————————————————————————————————
Prof.
Assoc. Prof.

26

Satoshi YAMASHITA
Ikuko FUNATOGAWA

Prof.
Assoc. Prof.

Koji KANEFUJI
Hisashi NOMA

Prof.
Assist. Prof.

Shigeyuki MATSUI
Nobuo SHIMIZU

Organization

Staff
Department of Statistical Data Science / Metric Science Group
Project Researcher

Hiroka HAMADA

Project Researcher

Yasuhiro TANAKA• Structure Exploration Group —————————————————————————————————————————————————————————————
Prof.
Assoc. Prof.

Ryo YOSHIDA
Stephen WU

Assoc. Prof.
Assist. Prof.

Jun ADACHI
Daisuke MURAKAMI

Assoc. Prof.
Assist. Prof.

Kenichiro SHIMATANI
Yoshihiro HAYASHI

Department of Statistical Inference and Mathematics
Director

Satoshi ITO• Mathematical Statistics Group ———————————————————————————————————————————————————————————————
Prof.
Assoc. Prof.
Visiting Prof.

Satoshi KURIKI
Shogo KATO
Akimichi TAKEMURA

Prof.
Assoc. Prof.

Yoshiyuki NINOMIYA
Takaaki SHIMURA

Prof.
Assoc. Prof.

Shuhei MANO
Keisuke YANO

• Learning and Inference Group —————————————————————————————————————————————————————————————
Prof.
Assoc. Prof.

Kenji FUKUMIZU
Masayuki HENMI

Prof.
Assoc. Prof.

Hironori FUJISAWA
Ayaka SAKATA

Assoc. Prof.

Daichi MOCHIHASHI• Mathematical Optimization Group —————————————————————————————————————————————————————————
Prof.
Assoc. Prof.

Satoshi ITO
Bruno FIGUEIRA LOURENÇO

Prof.

Shiro IKEDA

Assoc. Prof.

Mirai TANAKA

Risk Analysis Research Center
Director

Satoshi YAMASHITA

Satoshi YAMASHITA
Tomoko MATSUIProf.
Yoshiyuki NINOMIYAAssoc. Prof.
Kengo KAMATANI
Assoc. Prof.
Jiancang ZHUANG
Assoc. Prof.
Yumi TAKIZAWA
Assoc. Prof.
Ikuko FUNATOGAWA
Project Assoc. Prof. Takao KUMAZAWA
Project Assist. Prof. Tran Duc VU
Project Researcher Atsushi FUKAZAWA
Visiting Prof.
Masakazu ANDO
Visiting Prof.
Tadashi ONO
Visiting Prof.
Naoki SAKAI
Visiting Prof.
Kiyomi SHIRAKAWA
Visiting Prof.
Isao TAKABE
Visiting Prof.
Hiroshi TSUDA
Visiting Prof.
Hisayuki HARA
Visiting Prof.
Toshihiro HORIGUCHI
Visiting Prof.
Mihoko MINAMI
Visiting Prof.
Hirokazu YANAGIHARA
Visiting Prof.
Yasushi YOSHIDA
Visiting Prof.
Tetsuya IWASA
Visiting Assoc. Prof. Takaki IWATA
Visiting Assoc. Prof. Teppei OGIHARA
Visiting Assoc. Prof. Yuta KOIKE
Visiting Assoc. Prof. Seisho SATO
Visiting Assoc. Prof. Koyomi NAKAZAWA
Visiting Assoc. Prof. Keisuke FUKUIVice Director

Shogo KATO

Satoshi KURIKI
Atsushi YOSHIMOTO
Prof.
Kazuhiro MINAMI
Assoc. Prof.
Keisuke YANO
Assoc. Prof.
Kenichiro SHIMATANI
Assoc. Prof.
Takaaki SHIMURA
Project Assoc. Prof. Masayuki KUMON
Assist. Prof.
Daisuke MURAKAMI
Project Assist. Prof. Shingo OBATA
Project Researcher Yosihiko OGATA
Visiting Prof.
Shinsuke ITO
Visiting Prof.
Takashi KAMEYA
Visiting Prof.
Yo SHEENA
Visiting Prof.
Kazuyuki SUZUKI
Visiting Prof.
Satoshi TAKIZAWA
Visiting Prof.
Tetsuji TONDA
Visiting Prof.
Masaaki FUKASAWA
Visiting Prof.
Toshio HONDA
Visiting Prof.
Sadaaki MIYAMOTO
Visiting Prof.
Yoshiki YAMAGATA
Visiting Prof.
Takaaki YOSHINO
Visiting Prof.
Katsutoshi NAGASHIMA
Visiting Assoc. Prof. Bogdan Dumitru ENESCU
Visiting Assoc. Prof. Kenichi KAMO
Visiting Assoc. Prof. Masashi KONOSHIMA
Visiting Assoc. Prof. Junichi TAKAHASHI
Visiting Assoc. Prof Kazuyoshi NANJO

Koji KANEFUJI
Yoshinori KAWASAKIProf.
Shuhei MANO
Assoc. Prof.
Masayuki HENMI
Assoc. Prof.
Shogo KATO
Assoc. Prof.
Stephen WU
Project Assoc. Prof. Yuuki RIKIMARU
Project Assist. Prof. Xiong ZIYAO
Project Assist. Prof. Tadashi NAKANISHI
Project Researcher Petrillo GIUSEPPE
Visiting Prof.
Masao UEKI
Visiting Prof.
Aitaro KATO
Visiting Prof.
Yasutaka SHIMIZU
Visiting Prof.
Rinya TAKAHASHI
Visiting Prof.
Hideatsu TSUKAHARA
Visiting Prof.
Shunji HASHIMOTO
Visiting Prof.
Satoshi FUJII
Visiting Prof.
Hiroshi MATSUZOE
Visiting Prof.
Hitoshi MOTOYAMA
Visiting Prof.
Nakahiro YOSHIDA
Visiting Prof.
Toshinao YOSHIBA
Visiting Prof.
Hiroaki NAGAFUJI
Visiting Assoc. Prof. Yukihiko OKADA
Visiting Assoc. Prof. Takafumi KUBOTA
Visiting Assoc. Prof. Noriyoshi SAKUMA
Visiting Assoc. Prof. Dou XIAOLING
Visiting Assoc. Prof. Shunichi NOMURA

Prof.

Prof.

Prof.

Prof.

Prof.

Prof.

27

Research Center for Statistical Machine Learning
Director

Kenji FUKUMIZU

Kenji FUKUMIZU
Satoshi ITO
Prof.
Shuhei MANO
Prof.
Hideitsu HINO
Assoc. Prof.
Ayaka SAKATA
Assist. Prof.
Kohei HATTORI
Project Assist. Prof. Toshimitsu ARITAKE
Project Researcher Zheng NING
Visiting Prof.
Konstantin MARKOV
Visiting Assoc. Prof. Kei KOBAYASHI
Visiting Assoc. Prof. Makoto YAMADA

Vice Director

Tomoko MATSUI

Tomoko MATSUI
Shiro IKEDAProf.
Hironori FUJISAWA
Assoc. Prof.
Daichi MOCHIHASHI
Assoc. Prof.
Mirai TANAKA
Assist. Prof.
Masato SHIRASAKI
Project Assist. Prof. Kotaro SAKAMOTO
Visiting. Prof.
Masataka GOTO
Visiting Assoc. Prof. Masaaki IMAIZUMIVisiting Assoc. Prof. Sayaka SHIOTA
Visiting Assoc. Prof. Eiji MOTOHASHI

Yukito IBA
Satoshi KURIKI
Prof.
Kazuhiro MINAMI
Assoc. Prof.
Shinsuke KOYAMA
Assist. Prof.
Daisuke MURAKAMI
Project Assist. Prof. Yoichi MOTOTAKE
Project Assist. Prof. Hideto NAKASHIMA
Visiting. Prof.
Arthur GRETTON
Visiting Assoc. Prof. Shuichi KAWANO
Visiting Assoc. Prof. Tsutomu TAKEUCHI
Visiting Assoc. Prof. João Pedro Pedroso

Prof.

Prof.

Prof.

Prof.

Prof.

Prof.

Data Science Center for Creative Design and Manufacturing
Director

Ryo YOSHIDA
Hideitsu HINO
Assist. Prof.
Yoshihiro HAYASHI
Project Researcher Aiko TAKAHASHI
Project Researcher Minoru KUSABA
Visiting Assoc. Prof. Masaaki TAKADA

Ryo YOSHIDA

Vice Director

Hironori FUJISAWA

Hironori FUJISAWA
Shinya NAKANO
Project Assist. Prof. Liu CHANG
Project Researcher Yoh NOGUCHI
Visiting. Prof.
Junichiro SHIOMI

Kenji FUKUMIZU
Stephen WU
Project Assist. Prof. Yuta AOKI
Project Researcher Hironao YAMADA
Visiting. Prof.
Junko MORIKAWA

Prof.

Prof.

Prof.

Prof.

Assoc. Prof.

Assoc. Prof.

Research Center for Medical and Health Data Science
Director

Shigeyuki MATSUI

Satoshi YAMASHITA
Hisashi NOMA
Project Assoc. Prof. Mayumi OKA
Visiting. Prof.
Senichiro KIKUCHI
Visiting Prof.
Tosiya SATO
Visiting Prof.
Hisateru TACHIMORI
Visiting. Prof.
Satoshi HATTORI
Visiting Assoc. Prof. Takeshi EMURA
Visiting Assoc. Prof. Kazushi MARUO

Vice Director

Hisashi NOMA

Shigeyuki MATSUI
Masayuki HENMI
Visiting. Prof.
Manabu AKAZAWA
Visiting. Prof.
Ken KIYONO
Visiting. Prof.
Kunihiko TAKAHASHI
Visiting Prof.
Satoshi TERAMUKAI
Visiting Prof.
Michiko WATANABE
Visiting Assoc. Prof. Ryoichi KIMURA
Visiting Assoc. Prof. Shonosuke SUGASAWA

Shinto EGUCHI
Fumikazu MIWAKEICHI
Visiting Prof.
Yoichi M. ITO
Visiting. Prof.
Atsushi GOTO
Visiting. Prof.
Masataka TAGURI
Visiting Prof.
Nobuaki NISHIYAMA
Visiting Assoc. Prof. Chieko ISHIGURO
Visiting Assoc. Prof. Yasunori SATO
Visiting Assoc. Prof. Kengo NAGASHIMA

Prof.

Prof.

Project Prof.

Assoc. Prof.

Assoc. Prof.

Assoc. Prof.

School of Statistical Thinking
Vice Director

Yukito IBA

Prof.

Prof.

Yukito IBA
Kazuhiro MINAMIProf.
Shiro IKEDA
Assoc. Prof.
Masayuki HENMI
Assist. Prof.
Akifumi OKUNO
Assist. Prof.
Kei NOBA
Project Researcher Yusaku OHKUBO
Visiting Assoc. Prof. Kei TAKAHASHI

Prof.

Prof.

Prof.

Prof.

Director

Yoshinori KAWASAKI
Hironori FUJISAWA
Prof.
Hideitsu HINO
Assoc. Prof.
Kenichiro SHIMATANI
Assoc. Prof.
Ayaka SAKATA
Assist. Prof.
Masato SHIRASAKI
Project Assist. Prof. Kazuhei KIKUCHI
Visiting Assoc. Prof. Osamu KOMORI

Satoshi KURIKI

Project Prof.
Assoc. Prof.
Assist. Prof.
Assist. Prof.
Visiting Prof.

Satoshi KURIKI
Yoshiyuki NINOMIYA
Hiroko NAKANISHI
Keisuke YANO
Kohei HATTORI
Ryota YUASA
Masayuki YOKOYAMA

Center for Training Professors in Statistics
Director
Prof.

28

Yoshinori KAWASAKIProf.

Masato CHINO
Satoshi YAMASHITA

Project Prof.

Masato CHINO

Organization

Staff
Center for Training Professors in Statistics
Project Prof.
Project Prof.
Project Prof.
Project Prof.

Hiroko NAKANISHI
Yasunori SAWAMURA
Naoki KAMIYA
Kunio TANABE

Kazuo MUROTA
Naoto KUNITOMO
Project Prof.
Masakazu JIMBO
Project Assoc. Prof. Masayoshi TAKAYANAGI
Project Prof.

Project Prof.

Project Prof.

Project Prof.
Project Prof.
Assist. Prof.

Manabu IWASAKI
Hideki ORIGASA
Masahiro MIZUTA
Ryota YUASA

Center for Engineering and Technical Support
Director
Deputy Manager
Unit Leader of Computing Facilities Unit
Unit Leader of Information Resources Unit

Genta UENO
Kazuhiro NAKAMURA
Mitsuru HAYASAKA
Noriaki MIYAZONO

Vice Director

Kazuhiro MINAMI

Unit Leader of Computer Networking Unit
Unit Leader of Media Development Unit

Kazuhiro NAKAMURA
Akiko NAGASHIMA

Library
Head

Genta UENO

Administration Planning and Coordination Section
Chief Director

Hiroe TSUBAKI

Yoshinori KAWASAKI

Director of Evaluation Unit

Yoshinori KAWASAKI

Vice Director of Evaluation Unit

Yoshihiko MIYASATO

Director of Information and Public
Relations Unit

Satoshi YAMASHITA

Vice Director of Information and Public
Relations Unit

Yoshihiko MIYASATO

Director of Intellectual Property Unit

Satoshi YAMASHITA

Director of NOE Promotion Unit

Satoshi YAMASHITA

Director of Gender Equality Unit

Yoshinori KAWASAKI
Hiroe TSUBAKI

Director of Planning Unit

Director of International Affairs Unit

• URA Station ———————————————————————————————————————————————————————————————————————

Kozo KITAMURA

Motoi OKAMOTO

Keisuke HONDA

Tachikawa Administration Department
General Manager Hikaru SASAGAWA• General Affairs Division ————————————————————————————————————————————————————————————————————
Manager Mitsuo HAMADA

Deputy Manager

Hisayuki IGARASHI

Deputy Manager
Specialist
Head, Personnel Team

Yoshihiro YAMADA
Akiharu MIYAGIHead, General Affairs Team
Head, Labor Management Team

Ayako NARITA
Keiko SHIMIZU

Head, Graduate School Team• Financial Division ———————————————————————————————————————————————————————————————————
ManagerHead, General Affairs and Audit Team
Head, Budget and Account Settlement
Team (ISM)

Takashi BAN

Deputy Manager

Hiroaki ARAI

Hiroaki ARAI

Head, Budget and Account Settlement
Team (NIPR)

Takeshi KUWAHARA

Akiko MAEKAWA

Head, Assets Management andAcceptance Team

Taku SASAKI• Accounting Division ————————————————————————————————————————————————————————————————————
Manager
Specialist
Specialist
Head, Contract Team ( NIPR )Head, Facilities Team

Koji SAKAMOTO

Hiroshi TAKAGIKana KATO
Yumiko OKAWA
Takuya SAITO

Deputy Manager
Specialist
Head, Accounting Team
Head, Contract Team ( ISM )Junichi NAKATA
Hitoshi HIRAYAMA
Yuji HIRONAKA

29

StaffTachikawa Administration Department• Research Promotion Division ————————————————————————————————————————————————————————————————————
Manager

Masakazu ICHIKAWA
Yusuke KASAKAWA

Head, Research Promotion Team

Deputy Manager

Hiromi OBAMA

Head, Cooperative Research Team

Isao SAKUMA• NIPR Planning and Coordination Division —————————————————————————————————————————————————————————————
Manager

Tsuyoshi ABE

Deputy Manager

Hiroyasu KUMAGAI

Head, NIPR Planning and Coordination Team• ISM Planning and Coordination Division —————————————————————————————————————————————————————————————
ManagerHead, ISM Planning and Coordination Team

Mitsuo HAMADA

Deputy Manager

Hisayuki IGARASHI

Ayako NARITAInternational Advisory Board ( As of April 1, 2022)
University Chair Professor, Peking University
Song Xi CHEN
Professor, University College London
Arthur GRETTON
Juan Carlos JIMÉNEZ-SOBRINO Professor, Departamento de Matemática Interdisciplinaria, Instituto de Cibernética, Matemática y Física
Professor, Sungkyunkwan University
Jibum KIM

Gareth W. PETERS

Chair Professor of Actuarial Science, Chair Professor of Statistics for Risk and Insurance, Department of Statistics
and Applied Probability, University of California Santa Barbara

Evgeny SPODAREV

Professor, Ulm University

Council of The Institute of Statistical Mathematics ( As of April 1, 2022)
Keiko TAKAHASHI

Senior Researcher (Professor), Global Consolidated Research Institute for Science Wisdom,
Comprehensive Research Organization, Waseda University

Hiroshi MARUYAMA
Satoru IGUCHI
Fumiyasu KOMAKIYou SHIINA
Hiroshi SAIGO
Yasuhiro OMORIPFN Fellow, Preferred Networks, Inc.

Kikuo MAEKAWA

Professor, Spoken Language Division,
Director, Center for Corpus Development National Institute for Japanese Language and Linguistics
Adjunct Researcher, Artificial Intelligence Research Center, Department of Information Technology and Human Factors,
National Institute of Advanced Industrial Science and Technology

Hideki ASOH
Mihoko MINAMI
Yoshinori KAWASAKI
Satoshi YAMASHITA
Tomoko MATSUI
Kazuhiro MINAMI
Satoshi ITO
Genta UENO
Satoshi KURIKI
Kenji FUKUMIZU
Hironori FUJISAWA

Professor, National Astronomical Observatory of Japan, National Institutes of Natural Sciences

Professor, Graduate School of Information Science and Technology, The University of Tokyo
Professor, Dean of Faculty of Data Science, Shiga University
Professor, Faculty of Political Science and Economics, Waseda University
Professor, Faculty of Economics, University of Tokyo

Professor, Department of Mathematics, Faculty of Science and Technology, Keio University
Professor ( Vice Director-General, ISM )Professor ( Vice Director-General, ISM )Professor ( Director of Department of Statistical Modeling, ISM )Professor ( Director of Department of Statistical Data Science, ISM )Professor ( Director of Department of Mathematical Analysis and Statistical Inference, ISM )Professor ( Director of Center for Engineering and Technical Support, ISM )Professor ( Director of School of Statistical Thinking, ISM )Professor ( Department of Mathematical Analysis and Statistical Inference, ISM )Professor ( Department of Mathematical Analysis and Statistical Inference, ISM )Cooperative Research Committee ( As of April 1, 2022)
Toshikazu KITANO
Shido SAI
Takashi SEO

30

Professor, Department of Architecture, Civil Engineering and Industrial Management Engineering, Nagoya Institute of Technology
Professor, Department of Economics, Faculty of Economics, Okayama Shoka University
Professor, Department of Applied Mathematics, Faculty of Science Division I, Tokyo University of ScienceOrganization

Cooperative Research Committee
Nagatomo NAKAMURA
Hiroki MASUDA
Tomoko MATSUI
Kenichiro SHIMATANI
Masayuki HENMI
Hiroko NAKANISHI

Professor, Faculty of Economics and Business Administration, Sapporo Gakuin University
Professor, Department of Mathematical Sciences, Faculty of Mathematics, Kyushu University
Professor ( Director of Department of Statistical Modeling, ISM )Associate Professor ( Department of Statistical Data Science, ISM )Associate Professor ( Department of Statistical Inference and Mathematics, ISM )Project Professor ( Center for Training Professors in Statistics, ISM )Advisory Board of NOE Project ( As of April 1, 2022)
Satoshi ITOH
Naonori UEDA
Yasushi OKADA
Masafumi KAMACHI
Yoshimichi SATO
Yutaka SOEJIMA
Yoshiki YAMAGATA

Chief Coordinator, Foundation for Computational Science
Deputy Director, RIKEN Center for Advanced Intelligence Project (AIP)
President, The Japan Pharmaceutical Manufacturers Association
Director, Ocean Eyes Co., Ltd.

Dean and Professor, Department of Humanities, Kyoto University of Advanced Science
Director-General, Institute for Monetary and Economic Studies, Bank of Japan
Professor, Graduate School of System Design and Management, Keio University

Managing Committee of School of Statistical Thinking ( As of April 1, 2022)
Ken KUROKAWA
Manabu KOBAYASHI
Yoshiyuki TAKEUCHI
Yoshihiko NISHINO
Jinfang WANG
Satoshi KURIKI
Yukito IBA
Yoshinori KAWASAKI
Hiroko NAKANISHI

Vice Director/Professor, National Institute of Genetics
Professor, Center for Data Science, Waseda University
Associate Professor, Graduate School of Economics, Osaka University
Director, Advanced Analytics & AI Innovation Division, SAS Institute Japan, Ltd.
Professor/Dean, School of Data Science, Yokohama City University
Director ( School of Statistical Thinking, ISM )Vice Director ( School of Statistical Thinking, ISM )Professor ( Vice Director-General, ISM )Project Professor (Center for Training Professors in Statistics, ISM )Research Ethics Board ( As of April 1, 2022)
Specialist on epidemiology and social research

Masayuki KANAI

Professor, School of Human Sciences, Senshu University

Specialist on epidemiology and social research

Keiko SATO

Associate Professor, Department of Health Informatics Graduate School of
Medicine & School of Public Health, Kyoto University

Specialist in the field of ethics and law

Hitomi NAKAYAMA
Hiroe TSUBAKI
Yutaka KURIKI
Tadahiko MAEDAYoo Sung PARK
Hisashi NOMA

Lawyer, Kasumigaseki-Sogo Law Offices

Specialist in the field of ethics and law
Person in citizen’s position
Research education staff of ISM
Research education staff of ISM
Research education staff of ISM

Director-General, The Institute of Statistical Mathematics
Director, Social welfare corporation Kunitachi nursery school
Associate Professor ( Department of Data Science, ISM )Associate Professor ( Department of Data Science, ISM )Associate Professor ( Department of Data Science, ISM )Professor Emeritus ( As of April 1, 2022)
Sigeki NISIHIRA
Noboru OHSUMI
Masami HASEGAWA
Yasumasa BABA
Yosihiko OGATA
Takashi NAKAMURA
Ryozo YOSHINO

Tatsuzo SUZUKI
Masakatsu MURAKAMI
Yoshiyuki SAKAMOTO
Katsuomi HIRANO
Hiroe TSUBAKI
Yoshiyasu TAMURA
Shinto EGUCHI

Giitiro SUZUKI
Kunio TANABE
Takemi YANAGIMOTO
Masaharu TANEMURAGenshiro KITAGAWA
Tomoyuki HIGUCHI
Yoshihiko MIYASATO

Ryoichi SHIMIZU
Tadashi MATSUNAWA
Yoshiaki ITOH
Makio ISHIGURO
Nobuhisa KASHIWAGI
Junji NAKANO

31

History

1944

June

Based on a proposal submitted at an academic study conference in December 1943, the organization was
founded as an institute under the direct control of the Ministry of Education. This proposal aimed to provide
supervision for studies looking into the mathematical principles of probability and their application, and was
also intended to facilitate, unify and promote the publication of research results.

1947

April

The affiliated statistical specialists’ school was opened.

May

The Institute was divided into the 1st Research Dept. ( fundamental theories ), the 2nd Research Dept. (statisticaltheories for the natural sciences), and the 3rd Research Dept. ( statistical theories for the social sciences ).

1949

June

The Institute was placed under the control of the Ministry of Education because of the enforcement of the Ministryof Education Establishment Law.

1955

September

Reorganized into the 1st Research Dept. ( fundamental theories ), the 2nd Research Dept. ( natural and social
science theories ), and the 3rd Research Dept. ( operations research, statistical analysis theories ). The laboratory
system, comprising 9 laboratories and the research guidance promotion room, was adopted.

1969

October

1971

April

The 4th Research Dept. (informatics theories) was instituted.

1973

April

The 5th Research Dept. (prediction and control theories) was instituted.

1975

October

1979

November

1985

April

Repositioned as a National Inter-University Research Institute due to the regulation change. The new mission
includes providing facilities and skills to other universities, in addition to conducting cutting-edge research on
statistical mathematics. Accordingly, the institute was reorganized into four basic research departments (Fundamental Statistical Theory, Statistical Methodology, Prediction & Control, and Interdisciplinary Statistics ) andtwo strategic centers ( Statistical Data Analysis Center and Statistical Education & Information Center ). The
Statistical Technical Training Center was terminated.

1988

October

The Dept. of Statistical Science was instituted in the School of Mathematical and Physical Science, part of the
Graduate University for Advanced Studies ( SOKENDAI ).

1989

June

The Institute was reorganized as an Inter-University Research Institute based on the National School Establishment Law.

1993

April

The Planning Coordination Chief System was instituted.

1997

April

The affiliated Statistical Data Analysis Center was reorganized into the Center for Development of StatisticalComputing, and the Statistical Education and Information Center was reorganized into the Center for Information
on Statistical Sciences.

2003

September

2004

April

The Institute was reorganized into the Institute of Statistical Mathematics, part of the Research Organi­zation ofInformation and Systems of the Inter-University Research Institute based on the National University Corporation Law. The Planning Coordination Chief System was abolished and the position of Vice Director-General was
instituted instead. The Dept. of Statistical Science in the School of Mathe­matical and Physical Science, SOKENDAI, was reorganized. In addition, the Dept. of Statistical Science and the School of Multidisciplinary Scienceswere instituted.

2005

April

The research organization was reorganized into three research departments ( the Department of Statis­tical
Modeling, the Department of Data Science, and the Department of Mathematical Analysis and Statistical Inference ). The affiliated Center for Development of Statistical Computing, the Center for Information on Statistical
Sciences, and the Engineering and Technical Services Section were inte­grated into the Center for Engineeringand Technical Support. The Risk Analysis Research Center was instituted.

2006

April

The Administration Planning Coordination Unit was instituted.

2008

April

The Research Innovation Center was instituted.

A new office building was constructed in Minato Ward.

The 6th Research Dept. (statistical theories of human behavior) was instituted.
The Information Research Building was constructed.

The Prediction and Knowledge Discovery Research Center was instituted.

The Administration Planning and Coordination Unit was reorganized into the Administration Planning and Coordination Section ( hereafter APCS), within which the Intellectual Property Unit, the Evaluation Unit and the Information and Public Relations Unit were instituted.
2009

32

January

The Planning Unit was instituted within APCS.

October

The Institute was moved to 10-3 Midori-cho, Tachikawa, Tokyo.

2010

June

Officially opened the Akaike Guest House.

July

Reorganized the Administration Office to create the NIPR/ISM Joint Administration Office and launch the General
Service Center. The NOE Forwarding Unit ( now we call “NOE Promotion Unit” ) was instituted within APCS.

2011

January

Research and Development Center for Data Assimilation was instituted.
Survey Science Center was instituted.

2012

January

Research Center for Statistical Machine Learning, Service Science Research Center and School of StatisticalThinking were instituted.

2014

July
December

2017

The URA Station was instituted within the Planning Unit.
The Office of Female Researcher Development was instituted within the Planning Unit.

January

Survey Science Center and Service Science Research Center were closed.

July

Data Science Center for Creative Design and Manufacturing was instituted.

December

The International Affairs Unit were instituted, and the Gender Equality Unit, which had been within the Planning
Unit, reorganized within APCS.
The Intellectual Property Unit was reorganized as the Industry-Academia Collaboration and Intellectual Property
Unit within APCS.

2018

April

Research Center for Medical and Health Data Science was instituted.
The NIPR/ISM Joint Administration Office was reorganized as the Tachikawa Administration Department of
the Research Organization of Information and Systems ( ROIS ).

2019

March

2021

April

2022

January

Research and Development Center for Data Assimilation was closed.
ISM Planning and Coordination Division was instituted within Tachikawa Administration Department.
Center for Training Professors in Statistics was instituted.

33

The Institute of
Statistical Mathematics(

Bus stop
TachikawaShiyakusho

to Kamikitadai

Tachikawa City Hall
Bus stop ( Tachikawa Academic Plaza))

Bus stop (Saibansho-mae)
Courthouse
Local Autonomy
College
Tachikawa
Police Station
Disaster
MedicalCenterShowa Kinen Park

Ome Line

Takamatsu
National Institutefor Japanese Language
and Linguistics

Tama
MonorailTachikawa-kita

Tachikawa
Access to the ISM

Chuo Lineto Shinjuku,
Tokyo →

Tachikawa Bus— Tachikawa Academic Plaza bus stop
— 5 min walk from Saibansho-mae
or Tachikawa-Shiyakusho bus stop
Tama Monorail—10 min walk from Takamatsu Sta.
JR Chuo Line
—25 min walk from Tachikawa Sta.

10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan
Tel: +81- ( 0 ) 50-5533-8500 Fax: +81- ( 0 ) 42-527-9302

https://www.ism.ac.jp/